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arXivCarnegie Mellon LLM agents & planning

Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents

Selection-based Structured Reasoning replaces open-ended reasoning with selection among reusable candidates, cutting per-turn latency over 90% while matching leading small-model search agents' success rates.

Feiyu Gavin Zhu, Xiaoyu Zhu, Jiqi Yang, Rui Yang and 8 more

Published Oct 1, 2026 · 0 citations · ▲ 3 on Hugging Face · Code

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arXivCarnegie Mellon U Illinois Urbana-Champaign U California San Diego Embodied agents

Video2Skill: From Streaming Experience to Reusable Embodied Skills

Video2Skill benchmarks streaming embodied skill discovery, showing VLMs group manipulation events poorly and rarely expand skill libraries despite supervised fine-tuning.

Jianshu Zhang, Ce Zhang, Xiyuan Yang, Chenwei Xu and 5 more

Published Sep 29, 2026 · 0 citations · ▲ 11 on Hugging Face

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arXivU California San Diego Carnegie Mellon Multimodal reasoning

WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning

WM-VLM adds a world-model branch to vision-language models to generate intermediate visual states for spatial reasoning, outperforming baselines by up to 39.25 points on mental rotation tasks.

Yuheng Zha, Yilei Wang, Qiyue Gao, Junrong Chen and 4 more

Published Sep 28, 2026 · 0 citations · ▲ 11 on Hugging Face · Code ★ 1

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NAACL 2024U Washington Allen Institute for Artificial I Carnegie Mellon Summarization

P3Sum: Preserving Author’s Perspective in News Summarization with Diffusion Language Models

P3Sum uses diffusion language models to preserve authorial perspective in news summarization, outperforming autoregressive baselines on perspective fidelity.

Yuhan Liu, Shangbin Feng, Xiaochuang Han, Vidhisha Balachandran and 3 more

Published 2024 · 2 citations

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ACL 2024U Washington U California, Berkeley Hong Kong University of Science Carnegie Mellon Hallucination & factuality

Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration

Multi-LLM collaboration detects knowledge gaps to make LLMs abstain from wrong answers instead of hallucinating.

Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding and 2 more

Published 2024 · 37 citations

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ACL 2023U Washington Carnegie Mellon Xi'an Jiaotong Fairness & bias

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

Pretraining data political biases propagate through language models into unfair hate speech and misinformation detectors, reinforcing polarization.

Shangbin Feng, Chan Young Park, Yuhan Liu, Yulia Tsvetkov

Published 2023 · 138 citations

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NeurIPS 2026Carnegie Mellon U Maryland CMU LLM pretraining & scaling laws

Language Models Need Sleep

Sangyun Lee, Sean McLeish, Tom Goldstein, Giulia Fanti

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Stanford Learning theory

Bentkus-type asymptotic e-values

Diego Martinez Taboada, Ben Chugg, Aaditya Ramdas

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon CMU, Carnegie Mellon RL for LLMs

Are we really tilting? The mechanics of reward guidance in flow and diffusion models

Sanjit Dandapanthula, Nicholas Boffi

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon Mechanistic interpretability

Concept-Localized Generative Representations

Amanda Merkley

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026SpotlightNew York Carnegie Mellon U Illinois at Urbana-Champaign CMU & MBZUAI Quantization

Requential Coding: Measuring Model Compressibility by Coding Data Instead of Parameters

Shikai Qiu, Marc Finzi, Yujia Zheng, Kun Zhang and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon CMU, Carnegie Mellon School of Computer Science, Carn Deep RL

Implicit Goal Conditioning via Value Disaggregation

Shashwat Saxena, Mehul Goel, Sreyas Venkataraman, Sarvesh Patil and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Texas at Austin UT Austin Carnegie Mellon UCLA Google DeepMind Optimization

Optimizing Analytic Constants via AI-Guided Lean Proof Refinement

Rahul Saha, Alan Li, Anton Xue, Adam Klivans and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Southern California Intel Labs Northwestern Intel Carnegie Mellon Multi-agent LLM systems

TritonTune: LLM-Guided Multi-Agent Optimization of GPU Kernel Configurations

Shukai Duan, Mihai Capotă, Guixiang Ma, Hou-Jen Ko and 4 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Northeastern Carnegie Mellon Exploration

Markovian Experimental Design under Concept Drift

Ayberk Yarkin Yildiz, Lili Su, Carlee Joe-Wong, Edmund Yeh and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Tsinghua Carnegie Mellon Tsinghua University, Tsinghua Tsinghua University Efficient inference & serving

TokenRouter: Efficient Serving System for Token-Level LLM Routing

Tianyu Fu, Tengxuan Liu, Ruoxi Wang, Yixin Dong and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Nanjing University of Aeronautic Nanjing University of Aeronautic Central China Normal Carnegie Mellon Flow matching

ControlFlow3D: Distilling Multi-View Knowledge into Latent Flow Matching for Point Cloud Upsampling

Yuang Liu, Zhi Zuo, Zhengkai Zhao, Lirui Zhang and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon Unified multimodal models

GRAM: Group-wise Rank-Aware Modal Merging via Subspace Alignment

Di Wu, Shuaidong Pan

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026EPFL Carnegie Mellon CMU/MBZUAI/GenBio CMU, Cartesia AI Long-context modeling

Raven: High-Recall Sequence Modeling via Sparse Memory Routing

Arshia Afzal, Aviv Bick, Eric Xing, Volkan Cevher and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Independent Researcher Peking Amazon Carnegie Mellon U California, San Diego Web & GUI agents

From Click Imitation to Transition Equivalence: Rethinking Supervision for GUI Agents

Zhiming Lin, Tianxiang Xu, zizhao zhang, Yixue Liu and 5 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026MIT Carnegie Mellon Deep learning theory

Fast Approximate $\ell_p$ Chamfer Distance via Lopsided Embeddings and Structured JL

Ying Feng, David Woodruff

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026OralCarnegie Mellon UC Irvine U California, Irvine CMU, Strategy Robot, Optimized M Optimization

Follow the Regularized Leader Does Not Converge in Constrained Optimization

Ioannis Anagnostides, Ioannis Panageas, Nikolas Patris, Tuomas Sandholm

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon University, Seou Carnegie Mellon Seoul National U Florida Epic Games Vision-language models

MM-SCALE: Evaluating Evidence-Grounded Moral Judgment in Vision-Language Models

Eunkyu Park, Wesley Deng, Cheyon Jin, Matheus Kunzler Maldaner and 7 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026CMU, Carnegie Mellon Carnegie Mellon Amazon Adobe Google DeepMind Multimodal reasoning

ANCHOR: Audio-Visually Grounded Chain-of-Thought Reasoning Benchmark

Joel Julin, Souraja Kundu, Liza Dahiya, George Z Wei and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon CMU, Carnegie Mellon CMU Efficient inference & serving

Learning Hierarchical Patch Splitting Policies for Faster Vision Transformers

Aditya Gupta, Jean S Dandurand, Kai Qiu, Rohan Choudhury and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026MIT Carnegie Mellon New York OpenAI Massachusetts Institute of Techn Game playing

A Regularization-Based Approach to Public Belief State Search for Adversarial Games

Sobhan Mohammadpour, Samuel Sokota, Brandon Kaplowitz, Zico Kolter and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Dartmouth College Carnegie Mellon New York LLM reasoning & chain of thought

When Form Changes but Logic Doesn’t: Building Logic-invariant LLMs through Structures

Xuyuan Liu, Xinshuai Dong, Elynn Chen, Yujun Yan

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Technical University of Munich Technische Universität München Imperial College London U Oxford Institute of Computer Science, L Agent benchmarks & environments

MedFlowBench: Auditing Medical Agents in Full-Study Workflows

Weixiang Shen, Chengzhi Shen, Che Liu, Junde Wu and 10 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon U Pittsburgh Diffusion models

Discrete Diffusion Playground: A 2D Benchmark for Discrete Generative Models

Justin Shao, Sihyun Park, David Koes, Maria Chikina

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon Backdoors & data poisoning

Emergent Misalignment as Data-Mediated Transfer

Baris Askin, Muhammed Ustaomeroglu, Anupam Nayak, Gauri Joshi and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, Berkeley Peking U Science and Technology of Chin Carnegie Mellon Stanford Software engineering agents

Verify0: Can AI Agents Build Formally Verified Software Repositories?

Zhe Ye, Hantao Lou, Yuechun Sun, Peiyang Song and 7 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon U Delaware CMU, Carnegie Mellon Deep learning theory

Beyond Eigenfunctions: Divergence Principal Functions for Representation Learning

Ritabrata Ray, Sahil Dharod, Burak Varıcı, Nicholas Boffi and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Princeton CMU, Carnegie Mellon Carnegie Mellon Colfax International NVIDIA Efficient attention & state-space models

GPU Hierarchy Meets Structured Matrices: Fast Algorithms for State-Space Models

Berlin Chen, Caitlin Wang, Aakash Sunil Lahoti, Kevin Li and 7 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Stanford U Washington U Washington; Meta Carnegie Mellon Stanford University/Pinterest Unified multimodal models

M*: A Modular, Extensible, Serving System for Multimodal Models

Atindra Jha, Naomi Sagan, Keisuke Kamahori, Irmak Sivgin and 8 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon Controllable generation

GeoFidelity-Bench: Evaluating Block-Conditioned Geographic Fidelity of Street-View Generation

Kaizhen Tan

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Mohamed bin Zayed University of Carnegie Mellon MBZUAI&CMU CMU & MBZUAI Datasets & benchmarks

MEV: A Multi-Event Video Dataset for Long-Take Generation

Peiyuan Zhu, Shaoan Xie, Yifan Shen, Wen Tian and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon Washington University in St. Lou Vision backbones

SLVR: Structured Latent Visual Reasoning via Human-like Reasoning Flows

Albert Gao, Bing Xue, Andrea Zanette

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U California, Merced The Chinese University of Hong K ByteDance Inc.Carnegie Mellon Google DeepMind / UC Merced Video generation

Learning to Correct Geometry in Generated Videos

Weijie Lyu, Tiancheng SHEN, Xiangtai Li, Yujing Wang and 4 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026CMU, Carnegie Mellon National University of Singapore Carnegie Mellon DEVCOM Army Research Laboratory Google Deepmind / Harvard Navigation & SLAM

Trajectory Planning without Trajectory Data: A Manifold-Guided Approach

Silong Yong, Anji Liu, Cunxi Dai, Carl Busart and 4 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Rice CMU, Carnegie Mellon National University of Singapore School of Computer Science, Carn Efficient attention & state-space models

Vortex: Efficient and Programmable Sparse Attention Serving

Zhuoming Chen, Xinrui Zhong, Qilong Feng, Ranajoy Sadhukhan and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Department of Computer Science, U Cambridge California Institute of Technolo Caltech MIT LLM evaluation & benchmarks

We Need to Improve Benchmarks in AI for Mathematics

Simon Frieder, Jonas Bayer, Shi Zhuo Looi, Jacob Loader and 14 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Massachusetts Institute of Techn CMU Virginia Polytechnic Institute a Carnegie Mellon Representation & contrastive learning

Position: Neurosymbolic AI is a strong technical foundation for trustworthy, deployable AI by design

Chandler Squires, Yaqi Xie, Simon Stepputtis, Katia Sycara and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon U Washignton U Washington Ethics & social impact

Community-Centered AI is Feasible and Beneficial for Impacted Communities

Tzu-Sheng Kuo, Quan Ze Chen, Amy Zhang, Kenneth Holstein and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Representation & contrastive learning

Projection Learning: A Principled Way to Overcome Memorization in Distribution Learning

Lin Chen, Dejan Slepcev

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Stanford Carnegie Mellon AI oversight & deception

Corrupted Plans, Clean Traces: What Planning-Execution Decoupling Reveals About CoT Monitoring

Keertana Chidambaram, Andrew Ilyas, Vasilis Syrgkanis

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Vision-language models

CCDiff: Inverse Canonical Correlation Analysis for Discovering Visual Differences in Natural Language

Neelesh Bisht, Xingjian Li, Zihan Li, Bo Jiang and 4 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Oxford U Edinburgh Technical University of Munich Carnegie Mellon National University of Singapore Agent memory

Git Context Controller: Manage the Context of Agents by Agentic Git

Junde Wu, Minhao Hu, Jiayuan Zhu, Shengda Zhu and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026CMU, Carnegie Mellon Indian Institute of Technology D Indian Institute of Technology, Carnegie Mellon Mechanistic interpretability

The Unembedding Bottleneck: A Mechanistic Analysis of Single Digit Counting in LLMs

Satwik Sunnam, Raghav Magazine, Vatsalya Singh, Lavanya Kotha and 2 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon CMU, Carnegie Mellon U Washington Stanford Proteins

DUIL: Deep Unsupervised Inverse Learning for in situ Macromolecular Morphology Identification

Mostofa Rafid Uddin, Seonghui Min, Mahek Vora, Qifeng Wu and 2 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Beijing University of Aeronautic Carnegie Mellon Autoregressive generation

Localizing Concepts in Visual Autoregressive Models

Nanxiang Jiang, Yang Liu, Yuanhao Wang, Min Xu

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Northeastern Carnegie Mellon Privacy

Optimal Byzantine-resilient Federated Learning with User-level Differential Privacy

Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Controllable generation

Generative Conformal Prediction with Optimized Coverage Allocation

Minxing Zheng, Shixiang Zhu

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Southern University of Science a ByteDance Inc.Carnegie Mellon Efficient inference & serving

Learning Cost-Efficient Autoscaling for Latency-Constrained Disaggregated LLM Serving

Fu Luo, Binbin Chen, Qing Luo, Linhui Xu and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon U Notre Dame CMU, Carnegie Mellon U Pittsburgh U North Carolina at Chapel Hill LLM evaluation & benchmarks

Can Agents Price a Reaction? Evaluating LLMs on Chemical Cost Reasoning

Yuyang Wu, Yue Huang, Shuaike Shen, Xujian Wang and 7 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Cambridge Sanger Institute U Stellenbosch Carnegie Mellon U Cambridge Welcome Sanger Genomics & single-cell

PRISM: Phenotype-Resolved Inference in Single-Cell Mixed Models via Latent Disease States and Contextualized Differential Expression

Andrea Rubbi, Lama Salem, Caleb Ellington, Pietro Lió and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Texas at Arlington The University of Texas at Arlin Carnegie Mellon U Texas at Dallas U Georgia 3D vision & reconstruction

Towards Matrix-Parallel and Feature-Scalable 3D Gaussian Splatting Rendering on GPUs

Yangming Zhang, Siyi Wu, Jian Wang, Bingzhe Li and 4 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon AutoML & architecture search

DeltaMomentum: A Key-Value based Anisotropic Momentum Update via Delta Rule

Euijin Hong, Guannan Qu

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026UT Austin Massachusetts Institute of Techn RIKEN HKBU / RIKEN Carnegie Mellon Machine unlearning

On the Fragility of Latent Knowledge: Layer-wise Influence under Unlearning in Large Language Model

Jianing Zhu, Zongze Li, Chandler Squires, Qizhou Wang and 2 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Texas A&M University - Corpu Graph neural networks

Traversal-Invariant Positional Encoding for Serialized Graphs

Krish Mody, Sridhar Radhakrishnan, Chandra N Sekharan

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Northeastern Stanford Amazon Amazon Science Microsoft Synthetic data

Firefly: Illuminating Verified Real-world Tool Call Data Generation

Yuxuan Lu, Ziyi Wang, Yingzhou Lu, Yisi Sang and 11 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026McMaster Vector Institute Department of Computer Science, Carnegie Mellon MPI for Intelligent Systems, Tüb Agent benchmarks & environments

AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment

Eric Zhang, Terry J Zhang, Chijioke Ugwuanyi, Jerick Shi and 2 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Carnegie Mellon U Michigan - Ann Arbor U Michigan Model OS Stanford LLM pretraining & scaling laws

Witness Overlap: Directional Provenance Inside Open-Weight Model Families

Siyuan Li, Haoxuan Zeng, Xin Luo, Fernando Jia and 5 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Northeastern Carnegie Mellon Microsoft Research Northeastern University, China Fudan RL for LLMs

What are Key Factors for Updates in RL for LLM Reasoning?

Peidong Wang, Demi Wang, Xufang Luo, Jiahang Xu and 4 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026UC Berkeley Carnegie Mellon UC Berkeley, Transluce AI U California, Berkeley LLM reasoning & chain of thought

Time to Pay Attention! Understanding High Complexity Corpus Reasoning Tasks

Prasann Singhal, Amanda Bertsch, Jacob Steinhardt, Sewon Min

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Ohio State Carnegie Mellon Ohio State University, Columbus Feature attribution

Generalized Priority-Aware Shapley Value

GPASV extends priority-aware Shapley values to cyclic weighted priority graphs via penalized violations, yielding differing LLM ensemble valuations.

Kiljae Lee, Ziqi Liu, Weijing Tang, Yuan Zhang

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026AWS U Pennsylvania Carnegie Mellon LLM agents & planning

What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents

LLM research agents find high-performance models via compressed prompts and feedback, supporting a description-length explanation for limited overfitting.

Martin Bertran, Aaron Roth, Steven Wu

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Massachusetts Institute of Techn Carnegie Mellon Quantization

A Differentiable Interior-Point Method in Single Precision

Differentiable interior-point optimization uses alternative complementarity to keep linear systems spectrally bounded, enabling reliable single-precision solving and differentiation.

Jon Arrizabalaga, Kevin Tracy, Zac Manchester

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Purdue Carnegie Mellon CMU Backdoors & data poisoning

When the Same Coefficients Reach Different Places: Asymmetric Realizability in Transplanting Tokenizers across Large Language Models

Cross-vocabulary tokenizer transplantation exhibits asymmetric realizability, allowing identical reconstruction coefficients to stay inert in donor anchors yet yield high-salience outputs in base anchors, forming breaker tokens that survive weight merging and evade spectral filters and LoRA mitigati

Xiaoze Liu, Weichen Yu, Matt Fredrikson, Xiaoqian Wang and 1 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon CMU Ethics & social impact

Smooth Partial Lotteries for Stable Randomized Selection

Partial lotteries are unstable because small score changes cause large selection shifts, so the Clipped Linear Lottery uses Lipschitz-smooth probabilities to achieve near-optimal regret with better stability-utility tradeoffs.

Alexander Goldberg, Giulia Fanti, Nihar Shah

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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71%Highly rated

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NeurIPS 2026U Arkansas U Arkansas - Fayetteville Carnegie Mellon LLM agents & planning

Agentic Multi-Turn Reasoning: A Fairness Approach

Fair-MPO improves multi-turn agentic reasoning via multi-level preference optimization and a fairness objective that fixes long-horizon credit assignment and data imbalance, achieving state-of-the-art benchmark results.

Thanh-Dat Truong, Sankalp Pandey, Hugh Churchill, Jackson Cothren and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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72%Highly rated

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NeurIPS 2026Carnegie Mellon Georgia Institute of Technology College of William and Mary UBC | Vector Institute | Google Amazon Multi-agent LLM systems

AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent

AgentArk distills multi-agent reasoning into a single LLM via hierarchical strategies, achieving multi-agent performance with single-agent efficiency.

Yinyi Luo, Yiqiao Jin, Weichen Yu, Mengqi Zhang and 5 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 8 on Hugging Face · Code ★ 216

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NeurIPS 2026Brown Carnegie Mellon North Carolina State Clinical NLP & EHR

History-Aware Conformal Prediction Sets for Censored Time-to-Event Outcomes

History-Aware Prediction Sets (HAPS) construct conformal prediction sets for censored time-to-event outcomes using time-varying covariate histories, reducing interval lengths up to 75% while maintaining coverage among survivors.

Yuyao Wang, Alexander W Levis, Shu Yang, Larry Han

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Illinois. Urbana-Champaign U Illinois Urbana-Champaign Carnegie Mellon MIT LLM agents & planning

CTM-AI: A Blueprint for General AI Inspired by a Model of Consciousness

CTM-AI combines a consciousness model with foundation models to integrate diverse processors, achieving state-of-the-art results on multiple benchmarks.

Haofei Yu, Yining Zhao, Lenore Blum, Manuel Blum and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon Allegheny Health Network Highmark Health U North Carolina at Chapel Hill Carnegie Mellon University / Abr Medical imaging

Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation

Evaluation metrics for AI radiology reports are highly sensitive to reference reporting style, altering model rankings and revealing poor clinical interpretation decoupling.

Daniel P Jeong, Charles Q Li, Hossein Hosseiny, Nitya M Bhalla and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tongji Carnegie Mellon National University of Singapore Johns Hopkins Shanghai Artificial Intelligence LLM evaluation & benchmarks

FinReasoning: A Hierarchical Benchmark for Reliable Financial Research Reporting

FinReasoning hierarchically benchmarks LLMs on financial research via semantic consistency, data alignment, and insight, revealing closed-source models suit core reasoning, open-source models lack consistency, and financial models lack auditing skills.

Yiyun Zhu, Yidong Jiang, Ziwen Xu, Yinsheng Yao and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Southern California Carnegie Mellon USC Time series forecasting

Learning Fractional-Order Dynamics from a Single Trajectory

FO-GS estimates discrete-time fractional-order linear dynamics from one trajectory with both errors scaling as O(t^{-1/2}).

Xiaole Zhang, Ziyi Zhang, zehao zhao, Stephen Tu and 3 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Learning theory

Persuasive Prediction via Decision Calibration

Persuasive prediction learns decision-calibrated predictors from data without common priors, matching Bayesian persuasion utility with efficient algorithms.

Jingwu Tang, Jiahao Zhang, Fei Fang, Steven Wu

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Science and Technology of Chin Tencent Zhejiang School of data science, Fudan un Nankai Tokenization

SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation

SimCT recovers lost cross-tokenizer supervision by comparing multi-token continuations in on-policy distillation, improving reasoning and code generation over exact token matching.

Jie Sun, Mao Zheng, Mingyang Song, Qiyong Zhong and 5 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Oxford Meta FAIR Carnegie Mellon U Washington FAIR @ Meta Meta Superintelligence Labs Test-time compute

Social Choice Foundations for Simulation-Augmented Generation

SAGE formalizes efficient inference-time viewpoint simulation via metric proportional justified representation, proving small simulated pools and dynamic routing preserve approximate proportional representation for contentious queries.

Sonja Kraiczy, Smitha Milli, Ratip Emin Berker, Avinandan Bose and 5 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KAIST Carnegie Mellon School of Computer Science, Carn Korea Advanced Institute of Scie CMU, Carnegie Mellon LLM pretraining & scaling laws

Flow Map Language Models: One-step Language Modeling via Continuous Denoising

Continuous flow language models outperform discrete diffusion in quality and speed, and distilling their unique flow map enables one-step generation surpassing eight-step discrete diffusion.

Chanhyuk Lee, Jaehoon Yoo, Manan Agarwal, Sheel Shah and 5 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 4 on Hugging Face · Code ★ 172

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NeurIPS 2026Department of Computer Science, Princeton Carnegie Mellon U Toronto Meta Code generation

P$^{3}$: Joint Program-and-Proof Planning\\ for Verified Code Generation

P³ plans programs and proofs jointly from specifications before elaboration, outperforming sequential baselines by up to 11.2 points on verified generation benchmarks while reducing cost and time.

Zenan Li, Ziran Yang, Peiyang Song, Zhaoyu Li and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Princeton U California, Santa Cruz Carnegie Mellon UC Berkeley Long-context modeling

Learning What to Remember: Test-Time Training via Context Distillation

TTCD uses a long-window teacher to supervise a short-window student's fast weights via hidden-state discrepancy, allocating limited memory to future-relevant context and outperforming existing long-context methods with minimal architectural changes.

Zixuan Wang, Xingyu Dang, Rui-Jie Zhu, Zixin Wen and 3 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Stanford Learning theory

Intrinsic-dimension empirical Bernstein inequalities for bounded self-adjoint operators

Empirical Bernstein inequalities for bounded self-adjoint operators replace unknown variance with data-driven estimates and scale with intrinsic dimension for dimension-free, infinite-dimensional guarantees.

Diego Martinez Taboada, Aaditya Ramdas

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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ICML 2026WorkshopCarnegie Mellon CMU, Carnegie Mellon School of Engineering and Applie Explainable AI

Measuring Weak-to-Strong Legibility of Reasoning Models

This paper defines weak-to-strong legibility for reasoning models and argues existing efficiency metrics miss thoroughness needed for weak monitors.

Dani Roytburg, Shreya Sridhar, Daphne Ippolito

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Harvard University, Harvard Harvard Carnegie Mellon Google DeepMind Google Research Genomics & single-cell

How Post-Training Shapes Biological Reasoning Models

Continued pre-training aligns biological language, supervised fine-tuning improves in-domain but harms out-of-domain reasoning, and reinforcement learning recovers generalization when rewards align.

Lukas Fesser, Hanlin Zhang, Michelle M Li, Eric Wang and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face

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NeurIPS 2026Carnegie Mellon CMU, Carnegie Mellon Salesforce AI Research, Universi School of Computer Science, Carn LLM evaluation & benchmarks

PACE: A Proxy for Agentic Capability Evaluation

PACE predicts agentic benchmark scores from small, selected non-agentic test subsets via regression, achieving under 4% error and over 0.80 correlation at under 1% evaluation cost.

Yueqi Song, Lintang Sutawika, Jiarui Liu, Lindia Tjuatja and 7 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 18 on Hugging Face

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NeurIPS 2026Hohai Nanjing University of Science an The University of Manchester Guangdong OPPO Mobile Telecommun The Hong Kong University of Scie LLM agents & planning

A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression

TACO is a training-free framework that learns adaptive compression rules from terminal agent trajectories to filter noisy observations, improving accuracy by 1-4% and reducing token usage across benchmarks.

JinCheng Ren, Siwei Wu, Yizhi Li, Zhu and 9 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 24 on Hugging Face · Code ★ 47

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NeurIPS 2026Duke Department of Computer Science, City University of Hong Kong Carnegie Mellon Duke University, Duke Efficient inference & serving

LowRankArena: A Standardized Evaluation Platform for SVD-Based LLM Compression

LowRankArena standardizes SVD-based LLM compression evaluation and reveals that method rankings and speedups depend heavily on backbone and workload under aligned protocols.

Zishan Shao, Lixun Zhang, Kangning Cui, Wenhao Wu and 9 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 1 on Hugging Face

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NeurIPS 2026Stanford Carnegie Mellon Stanford Video generation

HumanScore: Benchmarking Human Motions in Generated Videos

HumanScore benchmarks human motion in AI videos via six metrics, revealing gaps between visual plausibility and biomechanical fidelity across 13 models.

Tiange Xiang, Yusu Fang, Tian Tan, Narayan Schütz and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon Nanyang Technological National Taiwan National University of Singapore RL for LLMs

CLR-voyance : Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics

CLR-voyance frames inpatient reasoning as a POMDP with outcome-grounded rubrics, yielding an 8B model scoring 84.91% on CLR-POMDP and outperforming larger medical reasoning models.

Aishik Nagar, Arun-Kumar Kaliya-Perumal, Yu-Hsuan Han, Andrew Sheng-Han Huang and 4 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Columbia NVIDIA Robot manipulation

PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

PointZero predicts full 3D point tracks from sparse tracks and RGB-D to learn transferable dynamics without robot actions, outperforming baselines on dynamics and manipulation tasks.

Bardienus Duisterhof, Kaifeng Zhang, Adam Hung, Bowen Wen and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Huazhong University of Science a Institute of automation, Chinese Jilin Institute of Automation, Chinese Carnegie Mellon Video understanding

Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection

Standard video backbone readouts suppress patch-level temporal dynamics needed to detect AI-generated videos; a lightweight velocity-gated patch profiling readout reaches 95.28 AUC on frozen backbones.

Manni Cui, Ziheng Qin, ZiAn Wang, Ruiqi Liu and 7 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Copenhagen Carnegie Mellon Texas A&M Bloomberg Autodesk Video-language models

Not Another Text Benchmark: Putting the “Visual" Back in Visual Question Answering for Large Video Models

Three visual benchmarks for video understanding expose large video model weaknesses when reasoning through visual queries instead of text options.

Rwiddhi Chakraborty, Yinong O Wang, Cheng Zhang, Fan Bai and 5 more

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026New York Carnegie Mellon Syracuse Columbia Google Deep RL

The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability

The Smart Buildings Control Suite is an open-source HVAC benchmark using multi-year data from 11 buildings and scalable simulators to test control policies across diverse climates and structures.

Judah Goldfeder, Victoria Dean, Zixin Jiang, Xuezheng Wang and 3 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Carnegie Mellon TikTok Morpheus AI CMU, Carnegie Mellon University at Buffalo, SUNY Video generation

MonarchRT: Efficient Attention for Real-Time Video Generation

Monarch-RT factorizes video diffusion attention via Monarch matrices to reach 95% sparsity without quality loss, enabling 16 FPS real-time generation on one GPU with 1.4-11.8x kernel speedups.

Krish Agarwal, Zhuoming Chen, cheng Luo, Yongqi Chen and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026CMU, Carnegie Mellon Carnegie Mellon Amazon U Michigan - Ann Arbor Duke RL for LLMs

AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs

AstraFlow is a dataflow-oriented RL system for agentic LLMs that decouples rollout, dataflow, and training to enable multi-policy collaborative training with 2.7x faster training.

Haizhong Zheng, Yizhuo Di, Jiahui Wang, Shuowei Jin and 6 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 15 on Hugging Face · Code ★ 105

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NeurIPS 2026Instinct Robotics, Carnegie Mell Carnegie Mellon Deep RL

Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction

ASCPO guarantees high-probability state-wise safety constraints in model-free RL without strong assumptions, significantly outperforming existing methods on continuous robot control tasks.

Weiye Zhao, Feihan Li

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon NVIDIA TikTok Federated learning

Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning

FedRevive revives stale asynchronous federated updates via server-side data-free knowledge distillation, accelerating training by 38.4% and boosting accuracy by 16.5%.

Baris Askin, Holger Roth, Zhenyu Sun, Carlee Joe-Wong and 2 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon School of Computer Science, Carn PhD student, Carnegie Mellon OctoML LLM pretraining & scaling laws

PithTrain: A Compact and Agent-Native MoE Training System

PithTrain is a compact agent-native MoE training framework that matches production throughput while reducing agent turns by 62% and GPU time by 64% on framework tasks.

Ruihang Lai, Hao Kang, Haozhan Tang, Akaash R Parthasarathy and 5 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 1 on Hugging Face

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NeurIPS 2026Carnegie Mellon CMU, Carnegie Mellon Peking Alignment & preference optimization

Back to Blackwell: Closing the Loop on Intransitivity in Multi-Objective Preference Fine-Tuning

PROSPER defines the Maximum Entropy Blackwell Winner and applies it to multi-objective preference fine-tuning, outperforming baselines on instruction following and chat benchmarks.

Jiahao Zhang, Lujing Zhang, Keltin Grimes, Zhuohao Yu and 2 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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